The literature also has proposed solutions that evaluate the robustness of HFL using centralized model aggregation approaches. In this sense, [1]} trains several HFL models to detect cyberattacks affecting IoT devices and considers several configurations of label flipping, data poisoning, and model cancelling attacks and model aggregation functions acting as countermeasures. These functions provide a significant improvement against malicious participants. Another example is the proposed in [2]}, where HFL unsupervised and supervised models are trained to detect cyberattacks affecting spectrum sensors. Malicious participants implementing data and model poisoning attacks and four aggregation functions acting as anti-adversarial mechanisms are considered to measure the model robustness. However, despite the contributions of previous work, there is a lack of work focused on vertical FL that combines a decentralized setting with the exploration of adversarial attacks. In this sense, the works present in the literature regarding attacks in VFL focus on feature inference and privacy issues but do not consider model-focused attacks trying to degrade the predictions [3]}, [4]}.
